Researches On Incomplete Fingerprint Recognition Based On Data Mining And Information Fusion

With the development of the public and individual safety applications (e.g., border crossing, transportation security, network logon, access control, etc.), identification technology becomes the current hot topic for researchers. For automatic identification, biometric recognition refers to the use of distinctive anatomical and behavioral characteristics. Due to its uniqueness, immutability, reliability and convenience for collection, biometrics, such as fingerprint, iris, palm, face, voice, has become one of the most effective personal identity recognition. Fingerprint has been applied to recognition for hundreds of years. Especially since the19th century, it has experienced a better development in scientific research field. Nowadays, fingerprint recognition is the most widely used biometric recognition with legal effects since it is highly feasible and practical.Currently, the acquired fingerprints often have defects in fingerprint recognition such as dirty parts, scars, creases and so on. Due to the large lose of texture and serious nonlinear deformation, it is actually difficult for the low quality incomplete fingerprint to be identified. However, in some special circumstances, incomplete fingerprint recognition is inevitable. Therefore, it is important to do some researches on this project.This thesis mainly determines four key aspects in terms of incomplete fingerprint recognition, mainly orientation field reconstruction, segmentation, Region Of Interest (ROI) extraction and matching. Our major contributions are briefly shown as follows:1. The algorithm of incomplete fingerprint orientation field estimation is improved and proposed.Fingerprint ridge orientation reflects the characteristics of the fingerprint texture. The computational accuracy of orientation field directly affects the results of fingerprint enhancement and matching. So orientation reconstruction is the first step for incomplete fingerprint recognition. Besides the noises, the dirty parts and the creases should have been removed or restored. Based on the existing algorithms, a novel descriptor, including local and global information, is proposed for the characteristics of incomplete fingerprint. According to the properties of competition of the local fingerprint, complementation and redundancy of the global fingerprint, the fusion information is provided for the next step, adaptively. Moreover, the algorithm of incomplete fingerprint orientation field estimation is proposed based on entropy theory. And the uncertainty of incomplete fingerprint is shown as the orientation entropy, with which the fingerprint orientation field of the incomplete area is re-computed and measured. And the fusion orientation field is used for pre-processing and matching.2. The fusion segmentation method is proposed for incomplete fingerprint based on data mining, machine learning and non-statistical pattern.The fingerprint image can be divided into two parts:(1) The foreground that the finger pressed on the sensor, which contains a lot of texture information and provides useful features for fingerprint classification and recognition.(2) The background that contains all kinds of noises. For incomplete fingerprint, fingerprint matching becomes very difficult due to the loss of useful information and serious nonlinear deformation. As a result, it is necessary for incomplete fingerprint segmentation to include the correct features in the foreground and simultaneously remove the wrong features in the background efficiently. The feature vector, based on the gray, the gradient dispersion, orientation entropy and orientation coherence, is defined for segmentation with Support Vector Machines (SVM). With a few training samples, the general classifier is attained in the complex image of incomplete fingerprint. Furthermore, according to the defects of SVM, the texture feature of non-statistical LBP is used to measure the correlation and competition of direction among neighborhoods. Finally, the texture description by LBP and the output of SVM based on the orientation entropy are combined together to get the optimal segmentation.3. The method based on orientation entropy is proposed for incomplete fingerprint to extract the Region of Interest (ROI).The global characteristics, core and delta, are invariant to translation, rotation, expansion and reduction of fingerprints. By measuring the similarity of global or local features in the stored templates and the query fingerprint image, it can be decided whether the two fingerprints are matched or not. Moreover, in matching, a reference point is defined as the point that has the maximum curvature in the most internal ridge. Usually, a core point, the topmost or bottommost point on the innermost recurving ridgeline, is used as such a reference point. And some matching methods are only executed on the ROI centered at the reference point. Therefore, the ROI determination relies on the accurate detection of the reference point. The proposed method is based on orientation entropy and Poincare Index to detect the reference point of fingerprints and extract ROI subsequently. Poincare Index is classic, but it has its defects. In order to extract the ROI. Poincare Index should be complemented by other algorithms to exclude pseudo core. Reflecting changes in the texture with the perspective of the orientation field, the proposed orientation entropy can provide useful information to extract the ROI.4. The fingerprint recognition algorithm based on information fusion is proposed.The matching is the key point in fingerprint recognition. Methods of fingerprint matching can be coarsely categorized into:minutia-based, correlation-based and hybrid-based. The minutia-based algorithms, which rely on the accurate extraction of minutia, are common method of identification. Correlation-based algorithms require less computational complexity than minutia-based methods, but they are vulnerable to variations in position, scale, and rotation. The hybrid-based methods use both minutia and feature information. They combine minutia and feature matching results to generate a final matching score, which is a simple addition. The incomplete fingerprint has serious loss of texture and nonlinear deformation, while the single matching algorithm is hard to identify the complex incomplete fingerprints. As a result, the sub-matching fusion is in-depth discussed and researched in fingerprint recognition. Besides, the fusion matching algorithm has been proposed for the incomplete fingerprint recognition using the fusion decision criterion of different matching algorithms.